准确地估计信号的到达角(Angle Of Arrival,AOA)为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息(Channel Frequency Response,CFR)扩展...准确地估计信号的到达角(Angle Of Arrival,AOA)为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息(Channel Frequency Response,CFR)扩展阵列天线的亚米级室内定位系统.首先,采集CFR信息进行AOA和信号到达时间(Time Of Arrival,TOA)的联合估计;其次,提出了一种基于AOA和TOA二维聚类信息的直射路径识别算法;另外,还提出了可视环境(Line Of Sight,LOS)以及非可视环境(Non Line Of Sight,NLOS)的识别算法,可以准确的判断出当前接收机相对发射机是处于LOS还是NLOS环境;最后,利用现有的三天线Wi-Fi设备在室内进行了测角以及定位测试,实验结果表明本文提出的定位系统在室内LOS和NLOS环境下分别可以达到中值误差为0.8m,1.3m的定位精度,可用于室内高精度定位.展开更多
A localization algorithm using distance and angle information is proposed in wireless sensor networks. Assuming that node axial orientations are unknown, all angles are measured to calculate the angle differences betw...A localization algorithm using distance and angle information is proposed in wireless sensor networks. Assuming that node axial orientations are unknown, all angles are measured to calculate the angle differences between two nodes viewed by the third one. Then, localization problems are formulated as convex optimization ones and all geometric relationships among different nodes in the communication range are transformed into linear or quadratic constraints. If all measurements are accurate, the localization problem can be formulated as linear programming （LP）. Otherwise, by incorporating auxiliary variables, it can be regarded as quadratic programming （QP）. Simulations show the effectiveness of the proposed algorithm.展开更多
随着信息技术的发展和日益增长的位置服务需求,室内定位技术经成为了当下最热门的定位研究领域之一。在室内定位中比较常用的算法包括信号到达时间(Time of Arrival,TOA)、信号到达时间差(Time Difference of Arrival,TDOA)、信号到达角...随着信息技术的发展和日益增长的位置服务需求,室内定位技术经成为了当下最热门的定位研究领域之一。在室内定位中比较常用的算法包括信号到达时间(Time of Arrival,TOA)、信号到达时间差(Time Difference of Arrival,TDOA)、信号到达角(Angle of Arrival,AOA)等。这些定位技术都依赖于客户端与Wi-Fi接入点(Access Point,AP)处于可视环境(Line of Sight,LOS),当客户端与AP处于非可视环境(Non Line of Sight,NLOS)时,会造成定位精度严重下降。基于此,本文在传统的多信号分类算法(Multiple Signal Classification,MUSIC)的基础上结合OFDM信号的子载波特性,提出一种基于仿射传播聚类的LOS/NLOS环境识别算法用于提高基于AOA的室内定位的精度。该算法分为两步,先对无线信号的AOA和TOA进行估计,其次利用聚类算法对信号的AOA和TOA信息进行聚类,利用聚类的结果判断当前环境属于LOS环境还是NLOS环境。仿真结果表明,本文提出的算法具有良好的识别性能。展开更多
文摘准确地估计信号的到达角(Angle Of Arrival,AOA)为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息(Channel Frequency Response,CFR)扩展阵列天线的亚米级室内定位系统.首先,采集CFR信息进行AOA和信号到达时间(Time Of Arrival,TOA)的联合估计;其次,提出了一种基于AOA和TOA二维聚类信息的直射路径识别算法;另外,还提出了可视环境(Line Of Sight,LOS)以及非可视环境(Non Line Of Sight,NLOS)的识别算法,可以准确的判断出当前接收机相对发射机是处于LOS还是NLOS环境;最后,利用现有的三天线Wi-Fi设备在室内进行了测角以及定位测试,实验结果表明本文提出的定位系统在室内LOS和NLOS环境下分别可以达到中值误差为0.8m,1.3m的定位精度,可用于室内高精度定位.
文摘A localization algorithm using distance and angle information is proposed in wireless sensor networks. Assuming that node axial orientations are unknown, all angles are measured to calculate the angle differences between two nodes viewed by the third one. Then, localization problems are formulated as convex optimization ones and all geometric relationships among different nodes in the communication range are transformed into linear or quadratic constraints. If all measurements are accurate, the localization problem can be formulated as linear programming （LP）. Otherwise, by incorporating auxiliary variables, it can be regarded as quadratic programming （QP）. Simulations show the effectiveness of the proposed algorithm.
文摘随着信息技术的发展和日益增长的位置服务需求,室内定位技术经成为了当下最热门的定位研究领域之一。在室内定位中比较常用的算法包括信号到达时间(Time of Arrival,TOA)、信号到达时间差(Time Difference of Arrival,TDOA)、信号到达角(Angle of Arrival,AOA)等。这些定位技术都依赖于客户端与Wi-Fi接入点(Access Point,AP)处于可视环境(Line of Sight,LOS),当客户端与AP处于非可视环境(Non Line of Sight,NLOS)时,会造成定位精度严重下降。基于此,本文在传统的多信号分类算法(Multiple Signal Classification,MUSIC)的基础上结合OFDM信号的子载波特性,提出一种基于仿射传播聚类的LOS/NLOS环境识别算法用于提高基于AOA的室内定位的精度。该算法分为两步,先对无线信号的AOA和TOA进行估计,其次利用聚类算法对信号的AOA和TOA信息进行聚类,利用聚类的结果判断当前环境属于LOS环境还是NLOS环境。仿真结果表明,本文提出的算法具有良好的识别性能。
